Sr. AI Systems Engineer- Agentic and Productivity Systems

The Opportunity The Creative Cloud Engineering organization is building the next generation of AI-powered engineering infrastructure to accelerate developer productivity and operational excellence across the Creative Cloud ecosystem. As we expand into AI-driven workflows across developer productivity and platform initiatives, we are looking for a Senior AI Systems Engineer who operates at the intersection of experimentation and production systems. This role focuses on designing, orchestrating, and operationalizing agent-based systems that improve engineering workflows across CI/CD, developer tooling, and operational diagnostics. This is not a research role and not a prompt-engineering role. This is a systems engineering role focused on building durable infrastructure. You will help build AI-native engineering capabilities that compound engineering velocity across Creative Cloud over time. ## What You'll Do ### Agentic Workflow Development - Design and prototype agent-based systems for engineering workflows such as CI diagnostics, code review automation, build failure triage, and autonomous debugging - Develop multi-agent orchestration patterns with structured state, memory, and control boundaries - Rapidly evaluate emerging AI frameworks, agent tooling, and developer AI platforms in real-world engineering environments ### AI Systems Infrastructure - Build reusable orchestration layers and service architectures for AI-powered engineering systems - Develop structured evaluation pipelines including trace-based evaluation and regression testing for agent behavior - Implement feedback loops and instrumentation that continuously improve AI system performance ### Production Hardening - Convert experimental workflows into secure, scalable, production-grade services - Implement observability, tracing, cost controls, and model routing - Ensure reliability, operational stability, and measurable impact of AI-powered systems ### Platform Strategy & Collaboration - Define internal standards for AI experimentation, evaluation, deployment, and monitoring - Partner with DevEx, CI/CD, and platform teams across Creative Cloud to embed AI-native capabilities - Build cohesive infrastructure that prevents tool sprawl and enables reusable AI productivity systems across teams ## What Success Looks Like - Production-grade AI agents integrated into engineering workflows and CI systems - A standardized evaluation and tracing framework adopted across Creative Cloud engineering teams - Measurable reductions in manual debugging, failure triage, and operational friction - Reusable AI infrastructure components leveraged across multiple engineering teams - A clear AI productivity roadmap aligned with Creative Cloud platform initiatives ## Required Qualifications - 8+ years of software engineering experience, with demonstrated depth in systems-level work - Strong systems engineering experience (Python, Go, TypeScript, or similar) - Experience building distributed systems, developer platforms, or infrastructure services - Experience integrating LLMs or AI APIs into production systems - Experience evaluating and integrating across multiple AI providers (e.g., AWS Bedrock, Anthropic, OpenAI) including cost optimization and capacity planning - Strong understanding of observability, metrics, logging, and tracing systems - Experience operating production services at scale ## Preferred Qualifications - Experience with agent frameworks (LangGraph, AutoGen, CrewAI, or similar) - Experience with embeddings, vector databases, or RAG architectures - Experience designing evaluation and benchmarking systems for AI workflows - Experience with CI/CD platforms, developer tooling, or build systems - Experience building internal developer productivity platforms - Familiarity with cost-aware model orchestration and multi-model routing ## Ideal Candidate Profile - Has built and shipped an AI-powered system end-to-end, not just integrated an API - Can show a prototype they took from experiment to production - Comfortable making infrastructure decisions with incomplete information - Has debugged LLM reliability issues in production (latency, cost, failure modes, concurrency limits) - Experimental but pragmatic — prototypes quickly, productionizes effectively - Focused on measurable engineering productivity impact, not technology for its own sake ## Why This Role Matters AI is transforming how software is built. This role will help establish AI-native engineering infrastructure across Creative Cloud, enabling developers to build, test, and operate software more efficiently. By turning early experimentation into durable platform capabilities, this role will drive long-term improvements in engineering productivity and operational excellence across Adobe. This role is foundational to building long-term AI leverage across the organization.

Adobe is the global leader in digital media and digital marketing solutions. Our creative, marketing and document solutions empower everyone – from emerging artists to global brands – to bring digital creations to life and deliver immersive, compelling experiences to the right person at the right moment for the best results.

In short, Adobe is everywhere, and we’re changing the world through digital experiences.